EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results

AN ALTERNATIVE VIEW OF LOUDSPEAKER NONLINEARITIES USING THE HILBERT-HUANG TRANSFORM

ABSTRACT This paper presents a new approach to nonlinear loudspeaker characterization using the Hilbert-Huang transform (HHT). Based upon the empirical mode decomposition (EMD) and the Hilbert transform, the HHT decomposes nonlinear signals into adaptive bases which reveal nonlinear effects in greater and more reliable detail than current approaches. Conventional signal decomposition techniques such as Fourier and Wavelet techniques analyse nonlinear distortion using linear transform theory. This restricts the nonlinear distortion to harmonic distortion. This work shows that real nonlinear loudspeaker distortion is more complex. HHT offers an alternate view through the cumulative effects of harmonics and intra-wave amplitude-and-frequency modulation. The work calls into question the interpretation of nonlinear distortion through harmonics and points towards a link between physical sources of nonlinearity and amplitude-and-frequency modulation. The work furthermore questions the suitability of traditional signal analysis approaches while giving weight to the use HHT analysis in future work

Advanced signal enhancement techniques with application to speech and hearing

This thesis was previously held under moratorium from 24th October 2011 until 24th October 2013.Advanced signal enhancement techniques with application to speech and hearing are presented that are applied to areas including adaptive noise cancellation (ANC) in noisy speech signals, single channel noise reduction for speech enhancement, voice activity detection (VAD) and noise reduction in binaural hearing aids. The performance enhancement of the new techniques over competing approaches is presented. For the domains of ANC and single channel noise reduction, the use of Empirical Mode Decomposition (EMD) is the underpinning technique employed. A novel approach to dual-channel speech enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is also presented, when a noise reference is available. The new SEAEMD system incorporates the multi-resolution approach EMD with ANC for effective speech enhancement in stationary and non-stationary noise environments. Two novel Empirical Mode Decomposition based filtering (EMDF) algorithms are presented for single channel speech enhancement. The first system is designed to be particularly effective in low frequency noise environments. The second generalized EMDF system is designed to operate under other noisy conditions, with results presented for babble noise, military vehicle noise and car interior noise. It is shown that the proposed EMDF techniques enhance the speech more effectively than current speech enhancement approaches that use effective noise estimation routines. Speech systems such as hearing aids require fast and computationally inexpensive signal processing technologies. A new and computationally efficient 1-dimensional local binary pattern (1-D LBP) signal processing procedure is designed and applied to (i) signal segmentation and (ii) the VAD problem. Both applications use the underlying features extracted from the 1-D LBP. The simplicity and low computational complexity of 1-D LBP processing are demonstrated. A novel binaural noise reduction system is presented for steering the focus direction of a hearing aid (2 microphones per hearing aid) to additional directions as well as 0/180 degrees. The system places a spatial null in the direction of the target speaker to obtain a noise estimate. The noisy speech signal is then filtered to perform noise reduction, and thus focus on the target speaker located at the desired direction. The results demonstrate its performance at attenuating multiple directional interferers.Advanced signal enhancement techniques with application to speech and hearing are presented that are applied to areas including adaptive noise cancellation (ANC) in noisy speech signals, single channel noise reduction for speech enhancement, voice activity detection (VAD) and noise reduction in binaural hearing aids. The performance enhancement of the new techniques over competing approaches is presented. For the domains of ANC and single channel noise reduction, the use of Empirical Mode Decomposition (EMD) is the underpinning technique employed. A novel approach to dual-channel speech enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is also presented, when a noise reference is available. The new SEAEMD system incorporates the multi-resolution approach EMD with ANC for effective speech enhancement in stationary and non-stationary noise environments. Two novel Empirical Mode Decomposition based filtering (EMDF) algorithms are presented for single channel speech enhancement. The first system is designed to be particularly effective in low frequency noise environments. The second generalized EMDF system is designed to operate under other noisy conditions, with results presented for babble noise, military vehicle noise and car interior noise. It is shown that the proposed EMDF techniques enhance the speech more effectively than current speech enhancement approaches that use effective noise estimation routines. Speech systems such as hearing aids require fast and computationally inexpensive signal processing technologies. A new and computationally efficient 1-dimensional local binary pattern (1-D LBP) signal processing procedure is designed and applied to (i) signal segmentation and (ii) the VAD problem. Both applications use the underlying features extracted from the 1-D LBP. The simplicity and low computational complexity of 1-D LBP processing are demonstrated. A novel binaural noise reduction system is presented for steering the focus direction of a hearing aid (2 microphones per hearing aid) to additional directions as well as 0/180 degrees. The system places a spatial null in the direction of the target speaker to obtain a noise estimate. The noisy speech signal is then filtered to perform noise reduction, and thus focus on the target speaker located at the desired direction. The results demonstrate its performance at attenuating multiple directional interferers

Adaptive empirical mode decomposition for signal enhancement with application to speech

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to signal enhancement using adaptive empirical mode decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform, which use predefined basis functions. The empirical mode decomposition (EMD) performs very well in such environments and it decomposes a signal into a finite number of data-adaptive basis functions, called intrinsic mode functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation in order to perform signal enhancement on an IMF level. In comparison to the conventional adaptive noise cancellation, the application of SEAEMD to speech gives rise to improved quality and lower level of residual noise

Spatial noise reduction in binaural hearing aids

Binaural hearing aids are configured to have a wireless transmission link between the left and the right hearing aid. These hearing aids use directional signal processing to improve speech intelligibility. Traditional hearing aids process their monaural microphone signals using differential microphone arrays (DMA) which have maximum sensitivity to target sources located directly in front or directly behind the user. However, in many hearing situations, the desired speaker azimuth varies from these two predefined directions

1-D local binary patterns for onset detection of myoelectric signals

This paper presents a new 1-D LBP (Local Binary Pattern) based technique for onset detection. The algorithm is tested on forearm surface myoelectric signals that occur due to lower arm gestures. Unlike other onset detection algorithms, the method does not require manual threshold setting and fine-tuning, which makes it faster and easier to implement. The only variables are window size, histogram type and the number of histogram bins. It is also not necessary to measure the properties of the signal during a quiescent period before the algorithm can be used. 1-D LBP Onset Detection is compared with single and double threshold methods and is shown to be more robust and accurate

Lower arm electromyography (EMG) activity detection using local binary patterns

This paper presents a new electromyography activity detection technique in which 1-D local binary pattern histograms are used to distinguish between periods of activity and inactivity in myoelectric signals. The algorithm is tested on forearm surface myoelectric signals occurring due to hand gestures. The novel features of the presented method are that: 1) activity detection is performed across multiple channels using few parameters and without the need for majority vote mechanisms, 2) there are no per-channel thresholds to be tuned, which makes the process of activity detection easier and simpler to implement and less prone to errors, 3) it is not necessary to measure the properties of the signal during a quiescent period before using the algorithm. The algorithm is compared to other offline single- and double-threshold activity detection methods and, for the data sets tested, it is shown to have a better overall performance with greater tolerance to the noise in the real data set used